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Volume 26
Issue 12
10.3390/s26123678
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research on Auxiliary Decision-Making System for Manned Underwater Vehicle Damage Management Based on Deep Reinforcement Learning

by
Qingchao Xu
1,2,
Hui Feng
1,2,*,
Haixiang Xu
1,2,*,
Fang Tang
3,
Yong Wang
1,2,
Yifeng Chen
1,2 and
Liping Zhou
1,2
1
Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan 430063, China
2
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
3
Wuhan Second Ship Design and Research Institute, Wuhan 430063, China
*
Authors to whom correspondence should be addressed.
Sensors 2026, 26(12), 3678; https://doi.org/10.3390/s26123678 (registering DOI)
Submission received: 2 April 2026 / Revised: 26 May 2026 / Accepted: 5 June 2026 / Published: 9 June 2026
(This article belongs to the Section Intelligent Sensors)
Versions Notes

Abstract

In underwater navigation, MUVs risk damage from obstacles and equipment. Effective damage management supports timely decisions and maximizes functionality recovery. Existing approaches can be roughly categorized into rule-based reasoning, case-based reasoning and expert systems. However, the primary limitation of the existing approaches is their inability to adapt to dynamically changing scenarios. In this paper, an auxiliary decision-making system (ADMS) for manned underwater vehicle (MUV) damage management based on deep reinforcement learning (DRL) is proposed to address the problem of cabin flooding. This system is designed to provide auxiliary decision-making in emergency situations and help preserve MUV vitality. Furthermore, a comprehensive States–Actions cluster encompassing various damage management measures for real damage scenarios is constructed and digitized. Moreover, several novel reward functions are developed to ensure the DRL model obtains a safe strategy with ADMS operations. Finally, the MUV buoyancy and stability vitality evaluation criteria are defined and analyzed. The simulation results show that the auxiliary decision-making measures given by the ADMS in the damage state are effective and rational. The evaluation criterion for buoyancy vitality can exceed 38%, while the criterion for stability vitality can surpass 92%, with an optimal value exceeding 99%.
Keywords: underwater safety; damage management; decision support; cabin flooding; deep reinforcement learning; deep Q-network underwater safety; damage management; decision support; cabin flooding; deep reinforcement learning; deep Q-network

Share and Cite

MDPI and ACS Style

Xu, Q.; Feng, H.; Xu, H.; Tang, F.; Wang, Y.; Chen, Y.; Zhou, L. Research on Auxiliary Decision-Making System for Manned Underwater Vehicle Damage Management Based on Deep Reinforcement Learning. Sensors 2026, 26, 3678. https://doi.org/10.3390/s26123678

AMA Style

Xu Q, Feng H, Xu H, Tang F, Wang Y, Chen Y, Zhou L. Research on Auxiliary Decision-Making System for Manned Underwater Vehicle Damage Management Based on Deep Reinforcement Learning. Sensors. 2026; 26(12):3678. https://doi.org/10.3390/s26123678

Chicago/Turabian Style

Xu, Qingchao, Hui Feng, Haixiang Xu, Fang Tang, Yong Wang, Yifeng Chen, and Liping Zhou. 2026. "Research on Auxiliary Decision-Making System for Manned Underwater Vehicle Damage Management Based on Deep Reinforcement Learning" Sensors 26, no. 12: 3678. https://doi.org/10.3390/s26123678

APA Style

Xu, Q., Feng, H., Xu, H., Tang, F., Wang, Y., Chen, Y., & Zhou, L. (2026). Research on Auxiliary Decision-Making System for Manned Underwater Vehicle Damage Management Based on Deep Reinforcement Learning. Sensors, 26(12), 3678. https://doi.org/10.3390/s26123678

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